Exploring the Impact of Virtual Reflection Groups on Advanced Practice Nurse Students During the COVID-19 Pandemic: Focus Group Study With Master’s Students
Notice bibliographique
Résumé
BACKGROUND: In the master's program of advanced practice nursing at a Norwegian university college, the learning activity reflection groups were converted into virtual reflection group (VRG) meetings during the COVID-19 pandemic. Regardless of the students' clinical practices in different hospitals, they could participate in the same VRG meeting on the web together with the educator from the university college, and the clinical supervisors were invited to participate. The students were in the process of developing the core competence required in their role as advanced practice nurses (APNs), and they had increased responsibility in the implementation of the VRG meetings. OBJECTIVE: In this study, we aimed to explore how master's students of advanced practice nursing experienced VRG meetings during the COVID-19 pandemic. METHODS: A qualitative exploratory design was adopted using focus group interviews. A group of students in the master's program of advanced practice nursing participated in an interview that lasted for 60 minutes. They had experienced participating in the VRG meetings following a rigorous guide during their clinical practice. The data from the focus group were analyzed using qualitative content analysis. RESULTS: The main findings of this study highlighted the importance of structure in VRG meetings, the role of increased responsibility in students' learning processes, the development of APN students' competencies, and increased professional collaboration with clinical supervisors. The APN students and clinical supervisors also continued their discussions in the clinical setting afterward, which strengthened the collaboration between students' education in the master's program and their clinical practice. CONCLUSIONS: VRG meetings gave the students the opportunity to lead professional discussions while reflecting thoroughly on the chosen patient cases from clinical practice. They experienced receiving feedback from fellow students, supervisors, and educators as stimulating their critical thinking development.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».